Predicting the occurrence of surgical site infections using text mining and machine learning
- DOI
- 10.1371/journal.pone.0226272
- Published
- 2019-12-13
- Container
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0226272,
title = {Predicting the occurrence of surgical site infections using text mining and machine learning},
author = {Daniel A. da Silva and Carla S. ten Caten and Rodrigo P. dos Santos and Flavio S. Fogliatto and Juliana Hsuan},
year = {2019},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0226272},
url = {https://doi.org/10.1371/journal.pone.0226272}
}RIS
TY - JOUR TI - Predicting the occurrence of surgical site infections using text mining and machine learning AU - Daniel A. da Silva AU - Carla S. ten Caten AU - Rodrigo P. dos Santos AU - Flavio S. Fogliatto AU - Juliana Hsuan PY - 2019 JO - PLOS ONE DO - 10.1371/journal.pone.0226272 UR - https://doi.org/10.1371/journal.pone.0226272 ER -
APA
Silva, D. A. D., Caten, C. S. T., Santos, R. P. D., Fogliatto, F. S., & Hsuan, J. (2019). Predicting the occurrence of surgical site infections using text mining and machine learning. PLOS ONE. https://doi.org/10.1371/journal.pone.0226272
Source records
- crossref · retrieved 2026-09-25T10:04:34.463Z